Nonlinear dynamics

The Chair in Nonlinear Dynamics recruits a doctoral researcher to develop interpretable, data-driven models for complex physical systems. Work centres on spectral submanifold methods and nonlinear model reduction, with possible applications to fluid–structure interaction, materials with memory, soft robotics, teleoperation, space structures and turbulence. The doctorate can be based in mechanical engineering or mathematics according to the candidate’s background. The start is flexible, with an early start preferred. Apply online with a CV, bachelor’s and master’s transcripts and at least two r

Build a programmable network of coupled Kerr parametric oscillators and measure its nonlinear dynamics. Experiments will map attractors, switching and probability currents and compare measured trajectories with simulations and analytical models.

…scientific computing, optimization, nonlinear dynamics, mathematical physics, network science and multiscale simulation. Faculty teach, supervise students, seek research funding and contribute to curriculum development. English is the working language. Applications received before 1 November 2026 receive full consideration; the search continues until filled.

Neuroscientific data exhibits complex, nonlinear dynamics, varying in time and over trials. A growing body of work has focused on capturing nonlinear dynamics, doing so in increasingly complex ways for modeling and inference. Meanwhile, capturing the dependency on experimental conditions surrounding the neural recordings remains understudied, and few methods leverage the correlation structure across experimental conditions. This poses difficulties for model estimation as well as any analyses comparing and interpolating between conditions. In this work, we develop the Gaussian Process Linear Dy

…in terms of an underlying system of nonlinear dynamics. A classical approach has been dynamical systems theory, whereby biophysical processes in the brain can be naturally formalized in terms of differential equations (ODEs) that may be capable of offering an explicit and interpretable account of how different biochemical and physiological processes in the brain evolve to produce the complex repertoire

Cognitive and motor functions of the human brain often display non-linear dynamics. Fractal dimension such as Higuchi’s fractal dimension (HFD) has been used as an effective method to probe these nonlinear dynamics and has been used to characterise different diseases. However, there is inconsistency in how HFD varies with age and disease in literature, which could be due to different computational techniques or frequency ranges used in these studies. To resolve this disparity, we investigated the frequency-dependant change of HFD with age for 64-channel electroencephalogram (EEG) data recorded

Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how neural activity relates to perception and behavior. Models of neural dynamics often focus on either low-dimensional projections of neural activity, or on learning dynamical systems that explicitly relate to the neural state over time. We discuss how these two approaches are interrelated by considering dynamical systems as representative of flows on a low-dimensional manifold. Building on this concept, we propose a new decomposed dynamical system model that represents com

Electrophysiological recordings during perceptual decision tasks in monkeys suggest that the degree of confidence in a decision is based on a simple neural signal produced by the neural decision process. Attractor neural networks provide an appropriate biophysical modeling framework, and account for the experimental results very well. However, it remains unclear whether attractor neural networks can account for confidence reports in humans. We present the results from an experiment in which participants are asked to perform an orientation discrimination task, followed by a confidence judgment.

…a key building block of neural circuit dynamics. While previous models of this transformation vary widely in their complexity, they all describe the under-lying functional architecture as unitary, performing a single type of elementary computation, either once, or cascaded multiple times. Here, we show that the input-output transformation of CA1 pyramidal cells during phase precession in the ‘theta-state’ is instead best captured by two distinct functional architectures operating in parallel. We use statistically principled methods to f